Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	21,396
2	New York	21,260
3	Arizona	20,698
4	New Jersey	20,216
5	Florida	17,674
6	Rhode Island	17,049
7	Massachusetts	16,586
8	District of Columbia	16,335
9	Mississippi	15,816
10	Alabama	14,646
11	South Carolina	14,574
12	Delaware	14,163
13	Connecticut	13,525
14	Maryland	13,354
15	Illinois	13,190
16	Georgia	13,102
17	Iowa	12,743
18	Texas	12,641
19	Nevada	12,583
20	Nebraska	12,141
21	Tennessee	12,029
22	Arkansas	11,679
23	Utah	11,128
24	California	10,693
25	North Carolina	10,033
26	Virginia	9,418
27	Idaho	9,174
28	South Dakota	9,130
29	Indiana	8,931
30	Minnesota	8,511
31	New Mexico	8,502
32	Pennsylvania	8,457
33	Michigan	8,400
34	Wisconsin	8,377
35	Kansas	8,367
36	Colorado	7,249
37	North Dakota	7,047
38	Oklahoma	6,899
39	Washington	6,755
40	Ohio	6,736
41	Missouri	6,265
42	Kentucky	5,633
43	New Hampshire	4,629
44	Puerto Rico	4,082
45	Wyoming	3,953
46	Oregon	3,659
47	Alaska	3,577
48	West Virginia	3,047
49	Maine	2,769
50	Montana	2,657
51	Vermont	2,189
52	Hawaii	997

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Louisiana	548
2	Mississippi	496
3	Florida	458
4	South Carolina	327
5	Texas	327
6	Alabama	326
7	Arizona	321
8	Nevada	317
9	Tennessee	291
10	Idaho	267
11	California	262
12	Georgia	261
13	Arkansas	222
14	Kansas	219
15	Missouri	179
16	Oklahoma	171
17	North Carolina	167
18	Utah	162
19	Wisconsin	161
20	Alaska	155
21	North Dakota	152
22	Iowa	147
23	Nebraska	146
24	New Mexico	136
25	District of Columbia	126
26	Washington	124
27	Kentucky	117
28	Virginia	115
29	Maryland	112
30	Ohio	108
31	Indiana	106
32	Minnesota	103
33	Illinois	101
34	Puerto Rico	101
35	Montana	95
36	Delaware	93
37	Wyoming	93
38	Colorado	90
39	Rhode Island	84
40	West Virginia	77
41	Pennsylvania	75
42	Oregon	67
43	Michigan	65
44	South Dakota	64
45	Massachusetts	38
46	New York	35
47	Connecticut	30
48	New Jersey	30
49	New Hampshire	22
50	Hawaii	12
51	Maine	8
52	Vermont	8

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,768
2	New York	1,656
3	Connecticut	1,235
4	Massachusetts	1,228
5	Rhode Island	941
6	District of Columbia	821
7	Louisiana	789
8	Michigan	639
9	Illinois	595
10	Maryland	563
11	Pennsylvania	556
12	Delaware	541
13	Mississippi	478
14	Indiana	425
15	Arizona	408
16	Colorado	307
17	Georgia	307
18	New Hampshire	295
19	Minnesota	282
20	New Mexico	281
21	Alabama	278
22	Ohio	276
23	Iowa	257
24	South Carolina	249
25	Florida	248
26	Virginia	240
27	Nevada	228
28	California	203
29	Washington	201
30	Missouri	194
31	North Carolina	164
32	Nebraska	162
33	Kentucky	156
34	Texas	155
35	Wisconsin	150
36	South Dakota	134
37	North Dakota	131
38	Tennessee	128
39	Arkansas	125
40	Oklahoma	119
41	Kansas	111
42	Vermont	89
43	Maine	87
44	Utah	81
45	Idaho	76
46	Oregon	64
47	Puerto Rico	57
48	West Virginia	56
49	Wyoming	43
50	Montana	39
51	Alaska	23
52	Hawaii	16

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	9
2	Louisiana	9
3	South Carolina	8
4	Mississippi	7
5	Nevada	6
6	Alabama	5
7	Florida	5
8	Texas	5
9	Georgia	4
10	New Mexico	3
11	Arkansas	2
12	California	2
13	Idaho	2
14	Indiana	2
15	Iowa	2
16	Missouri	2
17	North Carolina	2
18	Rhode Island	2
19	Tennessee	2
20	Colorado	1
21	Delaware	1
22	Illinois	1
23	Kansas	1
24	Maryland	1
25	Massachusetts	1
26	Montana	1
27	Nebraska	1
28	North Dakota	1
29	Ohio	1
30	Oklahoma	1
31	Pennsylvania	1
32	Utah	1
33	Wisconsin	1
34	Alaska	0
35	Connecticut	0
36	District of Columbia	0
37	Hawaii	0
38	Kentucky	0
39	Maine	0
40	Michigan	0
41	Minnesota	0
42	New Hampshire	0
43	New Jersey	0
44	New York	0
45	Oregon	0
46	Puerto Rico	0
47	South Dakota	0
48	Vermont	0
49	Virginia	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	137,185	1	99
Lake	Tennessee	100,057	2	99
Lee	Arkansas	97,889	3	99
Dakota	Nebraska	93,728	4	99
Buena Vista	Iowa	89,704	5	99
Davidson	Tennessee	26,541	115	96
Richland	South Carolina	15,829	365	88
Orange	California	9,996	800	74
York	South Carolina	9,150	902	71
Pierce	Washington	4,940	1557	50

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	2,915	5	99
Richland	South Carolina	279	653	79
Davidson	Tennessee	249	720	77
Orange	California	164	954	69
Pierce	Washington	134	1100	64
York	South Carolina	60	1611	48

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.